DocumentCode :
1422367
Title :
Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living
Author :
Zhu, Chun ; Sheng, Weihua
Author_Institution :
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
Volume :
41
Issue :
3
fYear :
2011
fDate :
5/1/2011 12:00:00 AM
Firstpage :
569
Lastpage :
573
Abstract :
In this paper, we address natural human-robot interaction (HRI) in a smart assisted living (SAIL) system for the elderly and the disabled. Two common HRI problems are studied: hand gesture recognition and daily activity recognition. For hand gesture recognition, we implemented a neural network for gesture spotting and a hierarchical hidden Markov model for context-based recognition. For daily activity recognition, a multisensor fusion scheme is developed to process motion data collected from the foot and the waist of a human subject. Experiments using a prototype wearable sensor system show the effectiveness and accuracy of our algorithms.
Keywords :
gesture recognition; handicapped aids; hidden Markov models; human-robot interaction; mobile robots; neural nets; sensor fusion; service robots; wearable computers; context-based recognition; daily activity recognition; hidden Markov model; human robot interaction; multisensor fusion; neural network; robot assisted living; smart assisted living system; wearable sensor-based hand gesture recognition; Accuracy; Artificial neural networks; Gesture recognition; Hidden Markov models; Humans; Robots; Sensors; Assisted living; hidden Markov models (HMNs); human-robot interaction (HRI); wearable computing;
fLanguage :
English
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4427
Type :
jour
DOI :
10.1109/TSMCA.2010.2093883
Filename :
5682421
Link To Document :
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